def test_complex_center_crop(shape, target_shape, named):
shape = shape + [2]
input = create_input(shape, named=named)
out_torch = transforms.complex_center_crop(input, target_shape).numpy()
assert list(out_torch.shape) == target_shape + [
2,
]
def __central_kspace_crop(self, kspace, masked_kspace, mask, sensitivity_map=None):
if self.crop is not None:
kspace, masked_kspace, mask = transforms.complex_center_crop(
[kspace, masked_kspace, mask], self.crop, contiguous=True)
if sensitivity_map is not None:
# TODO: Linear does not work yet in 4D, working with bilinear instead.
sensitivity_map = torch.nn.functional.interpolate(
sensitivity_map.permute(0, 3, 1, 2).rename(None),
size=self.crop,
mode='bilinear',
align_corners=True).permute(0, 2, 3, 1).refine_names(*sensitivity_map.names)
backprojected_kspace = self.backward_operator(kspace)
return kspace, masked_kspace, mask, backprojected_kspace, sensitivity_map